Machine data as the source of learning engagement in hands-on learning online

Yu-Ping Hsu

Instructional technology provides the capacity to address the needs of students with diverse cognitive skills and socialization needs. Learning experience is viewed as an important factor in learner engagement/motivation, and a contributor to learning in online instruction (Sims, 2003; Swartzwelder & Murphy, 2019; Chan, Wan & Ko, 2019).  Moore’s three types of learning interaction (Moore, 1989) included student-content interaction, student-student interaction, and student-faculty interaction; and have been used widely in the research literature.  Several studies have demonstrated that well-designed online interactivities can improve student’s learning experience (Svihla, 2015; Cain & Lee, 2016; Watkins, 2005; Herrington, Oliver & Reeves, 2003). However, the field has no clear agreement on how to measure these interactivities for improving learning experience in online instruction (Ekwunife-Orakwue and Teng, 2014; Walmsley-Smith, Machin and Walton, 2019).  Some assume that an analytics approach, using tracking data from behavioral and physiological responses (e.g., facial expressions, eye tracking, click-stream data) as evidence of involvement and attentiveness, is a measure of motivation and engagement.  Using the physiological response data in online instruction can be a reliable source of understanding online activities that enhance learning experience (Lee & Shapiro, 2019; Lee & DuMont, 2010).  The purpose of this project is to explore how to design learning activities in hands-on lessons online that are effective and engaging based on facial expressions and physiological responses. 

This publication uses Eye Tracking and Facial Expression Analysis which is fully integrated into iMotions Lab

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